Validate your edge.
Before it costs you.

Using data in your signal that wouldn't have been available at execution time — earnings, prices, or index changes known only in hindsight.

Testing on today's S&P 500 erases every stock that was delisted, went bankrupt, or was acquired — making history look far more tradeable than it was.

Optimizing parameters on the same data used to evaluate performance. The model learns noise as signal — numbers that will never repeat out-of-sample.

Commissions, SEC/FINRA fees, bid-ask spread, and slippage compound fast. A 0.1% per-trade edge disappears quickly at high turnover or in options strategies.

A strategy tuned on 2012–2019 low-vol data behaves entirely differently in rate-hike cycles, liquidity crises, or post-FOMC volatility environments.

Testing hundreds of parameter combinations on the same dataset guarantees false discoveries by chance. Without out-of-sample discipline, the result is noise.